PFAM Annotation Module¶

This notebook demonstrates the use of the two main methods from the pfam_annotation.py module in pyMut.

1. Import libraries and load data¶

In [1]:

import matplotlib.pyplot as plt

# Import pyMut
from pyMut import read_maf
from pyMut.utils.database import build_embedded_db, connect_db

# Load example data
maf_file = "../../../src/pyMut/data/examples/MAF/tcga_laml_VEP_annotated.maf.gz"
maf_data = read_maf(maf_file,assembly="37")

# Configure PFAM database
try:
    build_embedded_db(force_rebuild=False)
except:
    pass

import matplotlib.pyplot as plt # Import pyMut from pyMut import read_maf from pyMut.utils.database import build_embedded_db, connect_db # Load example data maf_file = "../../../src/pyMut/data/examples/MAF/tcga_laml_VEP_annotated.maf.gz" maf_data = read_maf(maf_file,assembly="37") # Configure PFAM database try: build_embedded_db(force_rebuild=False) except: pass

2025-08-01 01:11:46,272 | INFO | pyMut.input | Starting MAF reading: ../../../src/pyMut/data/examples/MAF/tcga_laml_VEP_annotated.maf.gz
2025-08-01 01:11:46,273 | INFO | pyMut.input | Loading from cache: ../../../src/pyMut/data/examples/MAF/.pymut_cache/tcga_laml_VEP_annotated.maf_d6a2449e9d8817c9.parquet
2025-08-01 01:11:46,304 | INFO | pyMut.input | Cache loaded successfully in 0.03 seconds

✅ Database already exists at /home/luisruimore/Escritorio/TFG/src/pyMut/data/resources/data.duckdb

2. Using the annotate_pfam() method¶

In [2]:

# Annotate variants with PFAM domains
maf_data = maf_data.annotate_pfam()

# Show annotation examples
if 'pfam_id' in maf_data.data.columns:
    pfam_examples = maf_data.data[
        maf_data.data['pfam_id'].notna()
    ][['Hugo_Symbol', 'Protein_Change', 'uniprot', 'aa_pos', 'pfam_id', 'pfam_name']].head(10)
    display(pfam_examples)

# Annotate variants with PFAM domains maf_data = maf_data.annotate_pfam() # Show annotation examples if 'pfam_id' in maf_data.data.columns: pfam_examples = maf_data.data[ maf_data.data['pfam_id'].notna() ][['Hugo_Symbol', 'Protein_Change', 'uniprot', 'aa_pos', 'pfam_id', 'pfam_name']].head(10) display(pfam_examples)

2025-08-01 01:11:52,254 | INFO | pyMut.analysis.pfam_annotation | UniProt resolution summary:
2025-08-01 01:11:52,254 | INFO | pyMut.analysis.pfam_annotation |    Total identifiers processed: 676
2025-08-01 01:11:52,255 | INFO | pyMut.analysis.pfam_annotation |    Direct accessions: 669
2025-08-01 01:11:52,255 | INFO | pyMut.analysis.pfam_annotation |    Resolved via short_name: 0
2025-08-01 01:11:52,255 | INFO | pyMut.analysis.pfam_annotation |    Resolved via external ID: 0
2025-08-01 01:11:52,256 | INFO | pyMut.analysis.pfam_annotation |    Unresolved: 7
2025-08-01 01:11:52,854 | INFO | pyMut.analysis.pfam_annotation | Variantes anotadas con PFAM: 317/775
2025-08-01 01:11:53,007 | INFO | pyMut.analysis.pfam_annotation | 
Final annotation summary:
2025-08-01 01:11:53,007 | INFO | pyMut.analysis.pfam_annotation |    Total variants processed: 2,091
2025-08-01 01:11:53,008 | INFO | pyMut.analysis.pfam_annotation |    UniProt identifiers resolved: 669/676
2025-08-01 01:11:53,008 | INFO | pyMut.analysis.pfam_annotation |    Variants with PFAM annotations: 317

	Hugo_Symbol	Protein_Change	uniprot	aa_pos	pfam_id	pfam_name
0	KIAA1529	p.T431T	None	431.0	PF00004	PF00004
1	KIAA1529	p.R581H	Q9BS26	581.0	PF00102	PF00102
2	TBC1D2	p.L593R	None	593.0	PF00102	PF00102
3	LPPR1	p.T325I	None	325.0	PF00581	PF00581
4	BAAT	p.T376M	O95347	376.0	PF00667	PF00667
5	FKTN	<NA>	None	NaN	PF01112	PF01112
6	ZNF462	p.R1680C	Q9Y2D5	1680.0	PF01821	PF01821
7	ACTL7A	p.G426R	None	426.0	PF02221	PF02221
8	UGCG	p.Y47Y	A0A1B0GUV7	47.0	PF06327	PF06327
9	C9orf43	p.T181A	Q13219	181.0	PF13087	PF13087

3. Using the pfam_domains() method¶

In [3]:

# Summary by PFAM domain
pfam_summary = maf_data.pfam_domains(
    summarize_by='PfamDomain',
    top_n=10,
    include_synonymous=False
)

if not pfam_summary.empty:
    display(pfam_summary)

    # Visualization
    plt.figure(figsize=(12, 6))
    plt.bar(range(len(pfam_summary)), pfam_summary['n_variants'])
    plt.xticks(range(len(pfam_summary)), pfam_summary['pfam_name'], rotation=45, ha='right')
    plt.ylabel('Number of variants')
    plt.title('Top 10 Most Affected PFAM Domains')
    plt.tight_layout()
    plt.show()

# Summary by amino acid position
aa_summary = maf_data.pfam_domains(
    summarize_by='AAPos',
    top_n=10,
    include_synonymous=False
)

if not aa_summary.empty:
    display(aa_summary)

# Summary by PFAM domain pfam_summary = maf_data.pfam_domains( summarize_by='PfamDomain', top_n=10, include_synonymous=False ) if not pfam_summary.empty: display(pfam_summary) # Visualization plt.figure(figsize=(12, 6)) plt.bar(range(len(pfam_summary)), pfam_summary['n_variants']) plt.xticks(range(len(pfam_summary)), pfam_summary['pfam_name'], rotation=45, ha='right') plt.ylabel('Number of variants') plt.title('Top 10 Most Affected PFAM Domains') plt.tight_layout() plt.show() # Summary by amino acid position aa_summary = maf_data.pfam_domains( summarize_by='AAPos', top_n=10, include_synonymous=False ) if not aa_summary.empty: display(aa_summary)

	pfam_id	pfam_name	n_genes	n_variants
138	PF04118	PF04118	8	7
51	PF00520	PF00520	6	6
186	PF13853	PF13853	4	5
152	PF07679	PF07679	4	5
36	PF00271	PF00271	3	5
15	PF00069	PF00069	3	4
35	PF00270	PF00270	4	4
13	PF00063	PF00063	3	3
10	PF00041	PF00041	3	3
216	PF21114	PF21114	3	3

	uniprot	aa_pos	pfam_id	pfam_name	n_variants	n_genes
0	A0A0B4J262	28.0	PF00431	PF00431	1	1
33	Q14678	405.0	PF00096	PF00096	1	1
35	Q5JUK3	266.0	PF00084	PF00084	1	1
36	Q6ZU80	28.0	PF08647	PF08647	1	1
37	Q8NEA6	741.0	PF03250	PF03250	1	1
38	Q8NFX7	87.0	PF03055	PF03055	1	1
39	Q8NHH1	374.0	PF00096	PF00096	1	1
40	Q8WXB1	274.0	PF05586	PF05586	1	1
41	Q92688	43.0	PF00134	PF00134	1	1
42	Q96FT7	17.0	PF00535	PF00535	1	1

In [4]:

# Try to build the embedded database
print("Setting up PFAM database...")
try:
    # This will create the database if it doesn't exist
    build_embedded_db(force_rebuild=False)
    print("✓ PFAM database configured successfully")
except Exception as e:
    print(f"⚠️  Error setting up database: {e}")
    print("We will continue with available functions...")

# Try to connect to the database
try:
    db_conn = connect_db()
    print("✓ Database connection established")

    # Check available tables
    tables = db_conn.execute("SHOW TABLES").fetchall()
    print(f"Available tables: {[table[0] for table in tables]}")

    db_conn.close()
except Exception as e:
    print(f"⚠️  Error connecting to database: {e}")
    db_conn = None

# Try to build the embedded database print("Setting up PFAM database...") try: # This will create the database if it doesn't exist build_embedded_db(force_rebuild=False) print("✓ PFAM database configured successfully") except Exception as e: print(f"⚠️ Error setting up database: {e}") print("We will continue with available functions...") # Try to connect to the database try: db_conn = connect_db() print("✓ Database connection established") # Check available tables tables = db_conn.execute("SHOW TABLES").fetchall() print(f"Available tables: {[table[0] for table in tables]}") db_conn.close() except Exception as e: print(f"⚠️ Error connecting to database: {e}") db_conn = None

Setting up PFAM database...
✅ Database already exists at /home/luisruimore/Escritorio/TFG/src/pyMut/data/resources/data.duckdb
✓ PFAM database configured successfully
✓ Database connection established
Available tables: ['meta', 'pfam', 'xref']